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Why Learning Rate Matters: Gradient Descent Convergence, Proven With a Closed-Form Equation (Ep:02.03)
A learning rate that’s too large doesn’t just train slower — it can make loss diverge to infinity, provably. Module 2 of From Zero to Agents derives the exact convergence…
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Derivatives and Gradients — The Mechanism Behind “Learning” (Ep:02.02)
Every neural network “learns” by following a gradient downhill. Module 2 of From Zero to Agents builds derivatives, partial derivatives, and the chain rule from first principles, verifies them against…
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Matrices — Organized, Simultaneous Dot Products (Ep:02.01)
A matrix is just organized, simultaneous dot products. Module 2 of From Zero to Agents builds matrix multiplication from first principles and uses it to fully decode the real attention…
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Vectors, Norms, and Dot Products Explained: The Math Behind Every Embedding (Ep.02.00)
Every embedding, similarity score, and attention weight in modern AI comes down to vector operations. Module 2 of From Zero to Agents builds vectors, norms, and dot products from first…
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Byte-Pair Encoding Explained: Why Tokenization Isn’t Just Splitting on Spaces (Ep.01.00)
Every episode of Module 00 quietly relied on .split() — text broken into whole words at whitespace, no questions asked. It was a deliberate simplification, flagged twice and never resolved.…
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What Is Intelligence, Really? (From Zero to Agents, Episode 00.01)
Last episode ended with a question. Here’s a definition worth pressure-testing (a very common first instinct — someone may say): “A system is intelligent if it can analyze requirements and…
